HCC-Small: Investigating and Supporting the Iterative and Exploratory Process of Applying Statistical Machine Learning

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Software engineering emerged out of a need to better address the real-world development of software, transforming the practice of software development from an art, into a craft, and eventually into an engineering discipline. In contrast, the effective application of statistical machine learning algorithms and techniques currently remains an art, based primarily in expert intuition and experience. This project is an integrated program of research and education targeting researcher, student, and practitioner application of statistical machine learning algorithms and techniques as tools for software development. The research focuses on the design of human-centered computing through the investigation of the needs of people applying statistical machine learning as a tool for software development, the creation of new methods and tools supporting the development and deployment of applications that use statistical machine learning, and the evaluation of these new methods and tools in supporting the complex and exploratory task of applying statistical machine learning. There are two primary foci in a new proposed toolset: explicit support for history and experimentation in an iterative and exploratory process and new opportunities for mixed-initiative tools to aid that process. By examining the application of statistical machine learning as a craft, this research enables the potential broad impact of statistical machine learning as a tool for software development in human-centered computing research and applications.